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Erschienen in:
Optimization of Network Mapping for Screening and Intrusion Sensing Devices Kapitel lesen Erstes Kapitel lesen

2024 | OriginalPaper | Buchkapitel

Towards Intelligent Attendance Monitoring for Scalable Organization with Hybrid Model Using Deep Learning

verfasst von : Akhilesh Kumar Srivastava, Chandrahas Mishra, Anurag Mishra, Atul Srivastava

Erschienen in: Cryptology and Network Security with Machine Learning

Verlag: Springer Nature Singapore

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Abstract

Face recognition technology using AI has seen paradigm shift in the evolving world. Automatic attendance monitoring using real-time face identification is a solution to handle attendance in any small/large as well as scalable organization. Traditional methods in the organization involve calling names or signing sheets with individuals, which is a very time-consuming process and provides insurance. This is also subjected to manual errors. Automation of attendance recording and monitoring through face recognition is a process of identifying the face for taking attendance by using the image of the human face as biometric parameter captured through a surveillance camera in the premise. This article presents an effective way of attendance monitoring by making use of deep learning technology and compares its results with the state-of-the-art approaches.

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Vorheriges Kapitel Analysis of Security Aspect in Cloud Implementation: A Case Study of Google Cloud Provider
Nächstes Kapitel NTPhish: A CNN-RNN Hybrid Deep Learning Model to Detect Phishing Websites
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Metadaten
Titel
Towards Intelligent Attendance Monitoring for Scalable Organization with Hybrid Model Using Deep Learning
verfasst von
Akhilesh Kumar Srivastava
Chandrahas Mishra
Anurag Mishra
Atul Srivastava
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
Cryptology and Network Security with Machine Learning

Print ISBN: 978-981-9706-40-2

Electronic ISBN: 978-981-9706-41-9

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-981-97-0641-9_39

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